[Health Risk Assessment of Heavy Metals in Soils of a City in Guangdong Province Based on Source Oriented and Monte Carlo Models].

Taking a city in Guangdong Province as the research area, the concentration and spatial distribution characteristics of heavy metals in the surface soil were studied to clarify the situation of soil heavy metal pollution and priority control factors, providing basic data for the prevention and control of soil heavy metal pollution in the city. The content characteristics of heavy metals in 221 soil samples in the city were analyzed, and the potential health risk assessment and source analysis were carried out through the Monte Carlo model, the potential health risk assessment (HRA) model, and the PMF receptor model. It was found that heavy metals ω(As), ω(Hg), ω(Cd), ω(Pb), ω(Cr), ω(Cu), ω(Ni), and ω(Zn) in the soil of the city were 18.16, 0.43, 1.46, 68.57, 98.34, 64.19, 26.53, and 257.32 mg·kg-1, respectively, with a moderate to high degree of variation. Except for Ni concentration, the soil concentrations of other heavy metal elements exceeded the background values of soil in Guangdong Province to a certain extent, and the concentrations of Cd and Zn exceeded the national secondary standards, resulting in severe heavy metal pollution; the main sources of heavy metals were industrial sources, and natural parent materials, lead battery manufacturing, transportation, artificial cultivation, and pesticide and fertilizer inputs also had an undeniable impact on the accumulation of heavy metals in the soil. Heavy metals in the soil had a certain degree of tolerable carcinogenic health risk for both children and adults, whereas non-carcinogenic risks could be ignored. The potential health risk of children was greater than that of adults, and the main exposure route was through oral intake. The input sources of pesticides and fertilizers and As should be the main controlling factors for the health risks of heavy metals in the city's soil, followed by mixed sources and Cr. There were differences in the spatial distribution characteristics and relative pollution levels of heavy metals, and it is necessary to deepen zoning monitoring and control, strengthen soil pollution prevention and control, and reduce human input of heavy metals in soil.

[Pollution Characteristics and Risk Assessment of Polycyclic Aromatic Hydrocarbons in Soils of Guangzhou].

In order to comprehensively study the pollution characteristics of polycyclic aromatic hydrocarbons （PAHs） in soils of Guangzhou， 222 topsoil samples were collected and analyzed. The ecological risk of soil PAHs pollution was evaluated using the effect interval low/median method （ERL/ERM） and the （BaP） toxicity equivalent method， and the health risk of soil PAHs pollution was evaluated using the lifelong cancer risk increment model. The source of PAHs was analyzed using the characteristic compound ratio method and PMF model. The results indicated that： the content of surface soil （∑16PAHs） in Guangzhou was 38-11 115 µg·kg-1， with an average of 526 µg·kg-1， and 16 types of polycyclic aromatic hydrocarbon monomers showed strong variation. There was a certain degree of ecological risk of PAHs in Guangzhou， and there was already a significant ecological risk of PAHs pollution in individual sampling points， which were generally in a state of mild pollution. Based on the results of the health risk assessment， the contribution rates of total cancer risk in both adults and children were presented as follows： skin contact > ingestion of soil > respiratory intake. The health risk of children was greater than that of adults， and the overall health risk was within an acceptable range. Source analysis showed that the main sources of soil PAHs in Guangzhou were coal （37.1%）； diesel （32%）； coking （17.3%）； and mixed sources of traffic emissions， biomass combustion， and petrochemical product volatilization （13.6%）. The overall source of soil PAHs belonged to mixed sources. The research results have enriched our understanding of the pollution status of PAHs in the surface soil of Guangzhou and are helpful in promoting soil pollution prevention and control actions.

The Association of Supplemental Nutrition Assistance Program Participation and Food Insufficiency among Households with Children in the United States during COVID-19.

BACKGROUND: As the expansion of Supplemental Nutrition Assistance Program (SNAP) benefits and pandemic emergency assistance programs ended in late 2021, little is known about subsequent trends in food insufficiency (FI) among households with children. OBJECTIVES: This research examined the association between SNAP participation and FI among households with children in the United States, particularly non-Hispanic Black (Black) and Hispanic households. METHODS: This cross-sectional analysis used Household Pulse Survey data collected from December 2021 to May 2022. Spatial analysis was conducted to visualize FI and SNAP participation rates across 50 states. With state SNAP policy rules as exogenous instruments and sociodemographic factors as control variables, 2-stage probit models were utilized to assess the SNAP and FI association among all (n = 135,074), Black (n = 13,940), and Hispanic households with children (n = 17,869). RESULTS: Approximately 13.9% [95% confidence interval (CI): 13.85%, 13.99%] of households experienced FI, and 20.4% (CI: 20.35%, 20.51%) received SNAP benefits. Among Black and Hispanic households, higher rates were observed, with 23.3% (CI: 23.12%, 23.4%) and 20.8% (CI: 20.61%, 20.95%) experiencing FI and 36.3% (CI: 36.1%, 36.5%) and 26.9% (CI: 26.61%, 27.13%) receiving SNAP benefits. These rates varied across states, ranging from 8% (Utah) to 21.1% (Mississippi) for FI and from 8.8% (Utah) to 32.7% (New Mexico) for SNAP participation. SNAP participants demonstrated a 12% lower likelihood of FI than nonparticipants (CI: -0.18, -0.05, P < 0.001). Among Black households, SNAP participants had a 29% lower likelihood of FI than nonparticipants (CI: -0.54, -0.03, P < 0.001). However, SNAP participation was not significant among Hispanic households (P = 0.99), nor did it narrow the FI gap between Hispanic and non-Hispanic households (P = 0.22). CONCLUSIONS: SNAP participation was associated with lower levels of FI among households with children, particularly for Black households. However, there was no significant association between SNAP participation and FI among Hispanic households with children.

Personalized driving assistance algorithms: Case study of federated learning based forward collision warning.

Current designs of advanced driving assistance systems (ADAS) mainly developed uniform collision warning algorithms, which ignore the heterogeneity of driving behaviors, thus lead to low drivers' trust in. To address this issue, developing personalized driving assistance algorithms is a promising approach. However, current personalization systems were mainly implemented through manually adjusting warning trigger thresholds, which would be less feasible for overall drivers as certain domain expertise is required to set personal thresholds accurately. Other personalization techniques exploited individual drivers' data to build personalized models. Such approach could learn personal behavior but requires impractical large-scale individual data collections. To fill up the gaps, self-adaptive algorithms for personalized forward collision warning (FCW) based on federated learning were proposed in this study. A baseline model was developed by long short-term memory (LSTM) for FCW. Federated learning framework was then introduced to collect knowledge from multiple drivers with privacy preserving. Specifically, a general cloud server model was trained by collecting updated parameters from individual vehicle server models rather than collecting raw data. Besides, a driver-specific batch normalization (BN) layer was added into each vehicle server model to address the heterogeneity of driving behaviors. Experiments show empirically that the proposed federated-based personalized models with the BN layer showed to have the best performance. The average modeling accuracy has reached 84.88% and the performance is comparable to conventional total data collection training approach, where the additional BN layer could increase the accuracy by 3.48%. Finally, applications of the proposed framework and its further investigations have been discussed.